Predicting Long-Term Earnings Growth from Multiple Information Sources

Posted: 28 Jan 2010 Last revised: 14 Jun 2017

See all articles by Zhan Gao

Zhan Gao

Lancaster University

Wan-Ting Wu

University of Massachusetts Boston

Date Written: August 20, 2013


While expected long-term earnings growth plays a pivotal role in valuation and investment applications, its common proxy, analysts' long-term growth forecasts (LTG), is well known for being over-optimistic. Guided by a stylized growth model, this paper uses three information sources to improve growth prediction — analysts' forecasts, stock prices, and financial statements. We find that the growth model using LTG, past earnings growth, the forward earnings-to-price ratio and past returns as predictors is unbiased and most accurate among the models considered in this paper. We further show that this growth prediction results in higher trading profits, more accurate equity predictions, and more reliable estimates of cost of equity. The findings suggest that this improvement in growth prediction leads to economically significant consequences in valuation and investment applications.

Keywords: Long-term growth, earnings growth prediction, analysts’ forecasts, equity valuation, valuation ratio

JEL Classification: G11, G17

Suggested Citation

Gao, Zhan and Wu, Wan-Ting, Predicting Long-Term Earnings Growth from Multiple Information Sources (August 20, 2013). International Review of Financial Analysis, Vol. 32, No. 1, 2014, Available at SSRN: or

Zhan Gao

Lancaster University ( email )

Lancaster LA1 4YX
United Kingdom
44-1524-593-151 (Phone)

Wan-Ting Wu (Contact Author)

University of Massachusetts Boston ( email )

100 Morrissey Boulevard
Boston, MA 02125
United States

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